Anything you can do in Python can be done in a Pipedream Workflow. This includes using any of the 350,000+ PyPi packages available in your Python powered workflows.

Pipedream supports Python v{{$site.themeConfig.PYTHON_VERSION}} in workflows.

::: warning Python steps are available in a limited alpha release.

You can still run arbitrary Python code, including sharing data between steps, send API requests using connected accounts, use Data Stores, and accessing environment variables.

However, you can't delay or retry steps, or take advantage of other features available in the Node.js environment at this time. If you have any questions please contact support. :::

1. Click the + icon to add a new step
2. Click Custom Code
3. In the new step, select the python language runtime in language dropdown

A new Python Code step will have the following structure, with a handler method and a pd argument passed into it:

def handler(pd: "pipedream"):
  # Exports a variable called message with contents "Hello, World!"
  pd.export("message", "Hello, World!")

The handler method is called during the step's execution, and the pd object contains helper methods to use Data Stores and make authenticated API requests to apps.

• Import data exported from other steps
• Export data to downstream steps
• Retrieve data from a data store
• Store data into a data store
• Access API credentials from connected accounts

You can use print at any time in a Python code step to log information as the script is running.

The output for the print logs will appear in the Results section just beneath the code editor.

You can use any packages from PyPi in your Pipedream workflows. This includes popular choices such as:

• requests for making HTTP requests
• sqlalchemyfor retrieving or inserting data in a SQL database
• pandas for working with complex datasets

To use a PyPi package, just include it in your step's code:

import requests

And that's it.

No need to update a requirements.txt or specify elsewhere in your workflow of which packages you need. Pipedream will automatically install the dependency for you.

Pipedream's package installation uses the pipreqs package to detect package imports and install the associated package for you. Some packages, like python-telegram-bot, use an import name that differs from their PyPI name:

pip install python-telegram-bot

vs.

import telegram

Use the built in magic comment system to resolve these mismatches:

# pipedream add-package python-telegram-bot

import telegram

We recommend using the popular requests HTTP client package available in Python to send HTTP requests.

No need to run pip install, just import requests at the top of your step's code and it's available for your code to use.

GET requests typically are for retrieving data from an API. Below is an example.

import requests

def handler(pd: "pipedream"):
  url = 'https://swapi.dev/api/people/1'

  r = requests.get(url)

  # The response is logged in your Pipedream step results:
  print(r.text)

  # The response status code is logged in your Pipedream step results:
  print(r.status)

import requests

def handler(pd: "pipedream"):
  # This a POST request to this URL will echo back whatever data we send to it
  url = 'https://postman-echo.com/post'

  data = {"name": "Bulbasaur"}

  r = requests.post(url, data)

  # The response is logged in your Pipedream step results:
  print(r.text)

  # The response status code is logged in your Pipedream step results:
  print(r.status)

You can also send files within a step.

An example of sending a previously stored file in the workflow's /tmp directory:

import requests

def handler(pd: "pipedream"):
  # Retrieving a previously saved file from workflow storage
  files = {'image': open('/tmp/python-logo.png', 'rb')}

  r = requests.post(url='https://api.imgur.com/3/image', files=files)

You can return HTTP responses from HTTP-triggered workflows using the pd.respond() method:

def handler(pd: 'pipedream'):
  pd.respond({
    'status': 200,
    'body': {
      'message': 'Everything is ok'
    }
  })

Please note to always include at least the body and status keys in your pd.respond argument. The body must also be a JSON serializable object or dictionary.

:::warning

Unlike the Node.js equivalent, the Python pd.respond helper does not yet support responding with Streams.

:::

:::tip Don't forget to configure your workflow's HTTP trigger to allow a custom response. Otherwise your workflow will return the default response. :::

A step can accept data from other steps in the same workflow, or pass data downstream to others.

In Python steps, data from the initial workflow trigger and other steps are available in the pd.steps object.

In this example, we'll pretend this data is coming into our workflow's HTTP trigger via POST request.

// POST <our-workflows-endpoint>.m.pipedream.net
{
  "id": 1,
  "name": "Bulbasaur",
  "type": "plant"
}

In our Python step, we can access this data in the exports variable from the pd.steps object passed into the handler. Specifically, this data from the POST request into our workflow is available in the trigger dictionary item.

def handler(pd: "pipedream"):
  # retrieve the data from the HTTP request in the initial workflow trigger 
  pokemon_name = pd.steps["trigger"]["event"]["name"]
  pokemon_type = pd.steps["trigger"]["event"]["type"]

  print(f"{pokemon_name} is a {pokemon_type} type Pokemon")

To share data created, retrieved, transformed or manipulated by a step to others downstream call the pd.export method:

# This step is named "code" in the workflow

def handler(pd: "pipedream"):
  r = requests.get("https://pokeapi.co/api/v2/pokemon/charizard")
  # Store the JSON contents into a variable called "pokemon"
  pokemon = r.json()

  # Expose the data to other steps in the "pokemon" key from this step
  pd.export('pokemon', pokemon)

Now this pokemon data is accessible to downstream steps within pd.steps["code"]["pokemon"]

::: warning You can only export JSON-serializable data from steps. Things like:

• strings
• numbers
• lists
• dictionaries :::

You can leverage any environment variables defined in your Pipedream account in a Python step. This is useful for keeping your secrets out of code as well as keeping them flexible to swap API keys without having to update each step individually.

To access them, use the os module.

import os
import requests

def handler(pd: "pipedream"):
  token = os.environ['TWITTER_API_KEY']

  print(token)

Or an even more useful example, using the stored environment variable to make an authenticated API request.

If an particular service requires you to use an API key, you can pass it via the headers of the request.

This proves your identity to the service so you can interact with it:

import requests
import os

def handler(pd: "pipedream"):
  token = os.environ['TWITTER_API_KEY']

  url = 'https://api.twitter.com/2/users/@pipedream/mentions'

  headers { 'Authorization': f"Bearer {token}"}
  r = requests.get(url, headers=headers)

  print(r.text)

:::tip There are 2 different ways of using the os module to access your environment variables.

os.environ['ENV_NAME_HERE'] will raise an error that stops your workflow if that key doesn't exist in your Pipedream account.

Whereas os.environ.get('ENV_NAME_HERE') will not throw an error and instead returns an empty string.

If your code relies on the presence of a environment variable, consider using os.environ['ENV_NAME_HERE'] instead. :::

You may need to exit a workflow early. In a Python step, just a raise an error to halt a step's execution.

raise NameError('Something happened that should not. Exiting early.')

All exceptions from your Python code will appear in the logs area of the results.

Sometimes you want to end your workflow early, or otherwise stop or cancel the execution of a workflow under certain conditions. For example:

• You may want to end your workflow early if you don't receive all the fields you expect in the event data.
• You only want to run your workflow for 5% of all events sent from your source.
• You only want to run your workflow for users in the United States. If you receive a request from outside the U.S., you don't want the rest of the code in your workflow to run.
• You may use the user_id contained in the event to look up information in an external API. If you can't find data in the API tied to that user, you don't want to proceed.

In any code step, calling pd.flow.exit() will end the execution of the workflow immediately. No remaining code in that step, and no code or destination steps below, will run for the current event.

def handler(pd: 'pipedream'):
  return pd.flow.exit()
  print("This code will not run, since pd.flow.exit() was called above it")

You can pass any string as an argument to pd.flow.exit():

def handler(pd: 'pipedream'):
  return pd.flow.exit('Exiting early. Goodbye.')
  print("This code will not run, since pd.flow.exit() was called above it")

Or exit the workflow early within a conditional:

def handler(pd: 'pipedream'):
  # Flip a coin, running $.flow.exit() for 50% of events
  if random.randint(0, 100) <= 50:
    return pd.flow.exit()
  
  print("This code will only run 50% of the time");

You can also store and read files with Python steps. This means you can upload photos, retrieve datasets, accept files from an HTTP request and more.

The /tmp directory is accessible from your workflow steps for saving and retrieving files.

You have full access to read and write both files in /tmp.

import requests

def handler(pd: "pipedream"):
  # Download the Python logo
  r = requests.get('https://www.python.org/static/img/python-logo@2x.png')

  # Create a new file python-logo.png in the /tmp/data directory
  with open('/tmp/python-logo.png', 'wb') as f:
    # Save the content of the HTTP response into the file
    f.write(r.content)

Now /tmp/python-logo.png holds the official Python logo.

You can also open files you have previously stored in the /tmp directory. Let's open the python-logo.png file.

import os

def handler(pd: "pipedream"):
  with open('/tmp/python-logo.png') as f:
    # Store the contents of the file into a variable
    file_data = f.read()

If you need to check what files are currently in /tmp you can list them and print the results to the Logs section of Results:

import os

def handler(pd: "pipedream"):
  # Prints the files in the tmp directory
  print(os.listdir('/tmp'))

:::warning The /tmp directory does not have unlimited storage. Please refer to the disk limits for details. :::